人脸生成（Face Generation）¶

在该项目中，你将使用生成式对抗网络（Generative Adversarial Nets）来生成新的人脸图像。

获取数据¶

该项目将使用以下数据集：

MNIST
CelebA

由于 CelebA 数据集比较复杂，而且这是你第一次使用 GANs。我们想让你先在 MNIST 数据集上测试你的 GANs 模型，以让你更快的评估所建立模型的性能。

如果你在使用 FloydHub, 请将 data_dir 设置为 "/input" 并使用 FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

探索数据（Explore the Data）¶

MNIST¶

MNIST 是一个手写数字的图像数据集。你可以更改 show_n_images 探索此数据集。

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7ff0a2f0d1d0>

CelebA¶

CelebFaces Attributes Dataset (CelebA) 是一个包含 20 多万张名人图片及相关图片说明的数据集。你将用此数据集生成人脸，不会用不到相关说明。你可以更改 show_n_images 探索此数据集。

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7ff0a2e84748>

预处理数据（Preprocess the Data）¶

由于该项目的重点是建立 GANs 模型，我们将为你预处理数据。

经过数据预处理，MNIST 和 CelebA 数据集的值在 28×28 维度图像的 [-0.5, 0.5] 范围内。CelebA 数据集中的图像裁剪了非脸部的图像部分，然后调整到 28x28 维度。

MNIST 数据集中的图像是单通道的黑白图像，CelebA 数据集中的图像是三通道的 RGB 彩色图像。

建立神经网络（Build the Neural Network）¶

你将通过部署以下函数来建立 GANs 的主要组成部分:

model_inputs
discriminator
generator
model_loss
model_opt
train

检查 TensorFlow 版本并获取 GPU 型号¶

检查你是否使用正确的 TensorFlow 版本，并获取 GPU 型号

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

输入（Input）¶

部署 model_inputs 函数以创建用于神经网络的占位符 (TF Placeholders)。请创建以下占位符：

输入图像占位符: 使用 image_width，image_height 和 image_channels 设置为 rank 4。
输入 Z 占位符: 设置为 rank 2，并命名为 z_dim。
学习速率占位符: 设置为 rank 0。

返回占位符元组的形状为 (tensor of real input images, tensor of z data, learning rate)。

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='z_dim')
    learning_rate = tf.placeholder(tf.float32, name='lr')
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

辨别器（Discriminator）¶

部署 discriminator 函数创建辨别器神经网络以辨别 images。该函数应能够重复使用神经网络中的各种变量。在 tf.variable_scope 中使用 "discriminator" 的变量空间名来重复使用该函数中的变量。

该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.25
    with tf.variable_scope('discriminator', reuse=reuse): 
        # 28x28x3
        x = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        x = tf.maximum(alpha * x, x)
        # 14x14x64
        
        x = tf.layers.conv2d(x, 128, 5, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha * x, x)
        # 7x7x128
        
        x = tf.layers.conv2d(x, 256, 5, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha * x, x)
        # 4x4x256

        x = tf.layers.conv2d(x, 512, 5, strides=1, padding='same')
        x = tf.layers.batch_normalization(x, training=True)
        x = tf.maximum(alpha * x, x)
        # 4x4x512
        
        x = tf.reshape(x, (-1, 4*4*512))
        logits = tf.layers.dense(x, 1)
        outputs = tf.sigmoid(logits)
        
    return logits, outputs


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

生成器（Generator）¶

部署 generator 函数以使用 z 生成图像。该函数应能够重复使用神经网络中的各种变量。在 tf.variable_scope 中使用 "generator" 的变量空间名来重复使用该函数中的变量。

该函数应返回所生成的 28 x 28 x out_channel_dim 维度图像。

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.25
    with tf.variable_scope('generator', reuse=(not is_train)):
        x = tf.layers.dense(z, 7*7*256)
        x = tf.reshape(x, (-1, 7, 7, 256))
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha * x, x)
        # 7x7x256
        
        x = tf.layers.conv2d_transpose(x, 128, 5, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha * x, x)
        # 14x14x128
        
        x = tf.layers.conv2d_transpose(x, 64, 5, strides=2, padding='same')
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha * x, x)
        # 28x28x64

        x = tf.layers.conv2d_transpose(x, 32, 5, strides=1, padding='same')
        x = tf.layers.batch_normalization(x, training=is_train)
        x = tf.maximum(alpha * x, x)
        # 28x28x32
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x, out_channel_dim, 5, strides=1, padding='same')
        # 28x28x3
        
        outputs = tf.tanh(logits)
        
        return outputs
    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

损失函数（Loss）¶

部署 model_loss 函数训练并计算 GANs 的损失。该函数应返回形如 (discriminator loss, generator loss) 的元组。

使用你已实现的函数：

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_logits_real, d_model_real = discriminator(input_real)
    d_logits_fake, d_model_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    return  d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

优化（Optimization）¶

部署 model_opt 函数实现对 GANs 的优化。使用 tf.trainable_variables 获取可训练的所有变量。通过变量空间名 discriminator 和 generator 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

训练神经网络（Neural Network Training）¶

输出显示¶

使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

训练¶

部署 train 函数以建立并训练 GANs 模型。记得使用以下你已完成的函数：

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

使用 show_generator_output 函数显示 generator 在训练过程中的输出。

注意：在每个批次 (batch) 中运行 show_generator_output 函数会显著增加训练时间与该 notebook 的体积。推荐每 100 批次输出一次 generator 的输出。

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr= model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])    
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    losses = []
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images})

                if steps % 20 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})
        
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    losses.append((train_loss_d, train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
                    
    return losses

MNIST¶

在 MNIST 上测试你的 GANs 模型。经过 2 次迭代，GANs 应该能够生成类似手写数字的图像。确保生成器 (generator) 低于辨别器 (discriminator) 的损失，或接近 0。

batch_size = 32
z_dim = 100
learning_rate = 0.001
beta1 = 0.9


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))

with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, 
          mnist_dataset.get_batches, mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2... Discriminator Loss: 1.0740... Generator Loss: 4.2653
Epoch 1/2... Discriminator Loss: 0.6248... Generator Loss: 6.8048
Epoch 1/2... Discriminator Loss: 0.5337... Generator Loss: 6.4106
Epoch 1/2... Discriminator Loss: 0.4572... Generator Loss: 2.9056
Epoch 1/2... Discriminator Loss: 1.2499... Generator Loss: 1.0471

Epoch 1/2... Discriminator Loss: 0.8006... Generator Loss: 2.6111
Epoch 1/2... Discriminator Loss: 0.4417... Generator Loss: 6.0640
Epoch 1/2... Discriminator Loss: 0.6605... Generator Loss: 5.4876
Epoch 1/2... Discriminator Loss: 0.4197... Generator Loss: 8.8884
Epoch 1/2... Discriminator Loss: 0.3511... Generator Loss: 7.2170

Epoch 1/2... Discriminator Loss: 0.3785... Generator Loss: 6.2146
Epoch 1/2... Discriminator Loss: 0.3364... Generator Loss: 6.5460
Epoch 1/2... Discriminator Loss: 0.3349... Generator Loss: 7.0402
Epoch 1/2... Discriminator Loss: 0.3359... Generator Loss: 6.9794
Epoch 1/2... Discriminator Loss: 0.3336... Generator Loss: 6.5773

Epoch 1/2... Discriminator Loss: 0.3331... Generator Loss: 7.1963
Epoch 1/2... Discriminator Loss: 0.3337... Generator Loss: 7.1780
Epoch 1/2... Discriminator Loss: 0.3327... Generator Loss: 7.3623
Epoch 1/2... Discriminator Loss: 0.3296... Generator Loss: 7.4070
Epoch 1/2... Discriminator Loss: 0.3319... Generator Loss: 6.9523

Epoch 1/2... Discriminator Loss: 0.3441... Generator Loss: 6.9959
Epoch 1/2... Discriminator Loss: 0.3302... Generator Loss: 7.1760
Epoch 1/2... Discriminator Loss: 0.3288... Generator Loss: 7.2385
Epoch 1/2... Discriminator Loss: 0.3335... Generator Loss: 7.2666
Epoch 1/2... Discriminator Loss: 0.3327... Generator Loss: 7.6950

Epoch 1/2... Discriminator Loss: 0.3292... Generator Loss: 7.7370
Epoch 1/2... Discriminator Loss: 0.3277... Generator Loss: 7.8969
Epoch 1/2... Discriminator Loss: 0.3267... Generator Loss: 7.7860
Epoch 1/2... Discriminator Loss: 0.3271... Generator Loss: 7.8362
Epoch 1/2... Discriminator Loss: 0.3277... Generator Loss: 7.9225

Epoch 1/2... Discriminator Loss: 0.3303... Generator Loss: 7.8919
Epoch 1/2... Discriminator Loss: 0.3271... Generator Loss: 7.8719
Epoch 1/2... Discriminator Loss: 0.3270... Generator Loss: 7.7383
Epoch 1/2... Discriminator Loss: 0.3261... Generator Loss: 8.1019
Epoch 1/2... Discriminator Loss: 0.3269... Generator Loss: 8.0441

Epoch 1/2... Discriminator Loss: 0.3278... Generator Loss: 8.2617
Epoch 1/2... Discriminator Loss: 0.3266... Generator Loss: 8.1029
Epoch 1/2... Discriminator Loss: 0.3265... Generator Loss: 8.0682
Epoch 1/2... Discriminator Loss: 0.3281... Generator Loss: 8.1627
Epoch 1/2... Discriminator Loss: 0.3265... Generator Loss: 8.1392

Epoch 1/2... Discriminator Loss: 0.3267... Generator Loss: 8.1744
Epoch 1/2... Discriminator Loss: 0.3288... Generator Loss: 8.4046
Epoch 1/2... Discriminator Loss: 0.3261... Generator Loss: 8.1948
Epoch 1/2... Discriminator Loss: 0.3282... Generator Loss: 8.1468
Epoch 1/2... Discriminator Loss: 0.3263... Generator Loss: 8.3028

Epoch 1/2... Discriminator Loss: 0.3271... Generator Loss: 8.4726
Epoch 1/2... Discriminator Loss: 0.3260... Generator Loss: 8.4616
Epoch 1/2... Discriminator Loss: 0.3260... Generator Loss: 8.6309
Epoch 1/2... Discriminator Loss: 0.3259... Generator Loss: 8.7279
Epoch 1/2... Discriminator Loss: 0.3274... Generator Loss: 8.4314

CelebA¶

在 CelebA 上运行你的 GANs 模型。在一般的GPU上运行每次迭代大约需要 20 分钟。你可以运行整个迭代，或者当 GANs 开始产生真实人脸图像时停止它。

batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.6


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    losses = train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
                   celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/10... Discriminator Loss: 1.7396... Generator Loss: 21.2650
Epoch 1/10... Discriminator Loss: 0.5931... Generator Loss: 3.7645
Epoch 1/10... Discriminator Loss: 2.5901... Generator Loss: 9.4026
Epoch 1/10... Discriminator Loss: 1.0092... Generator Loss: 1.4501
Epoch 1/10... Discriminator Loss: 0.6754... Generator Loss: 4.6773

Epoch 1/10... Discriminator Loss: 0.5806... Generator Loss: 5.0719
Epoch 1/10... Discriminator Loss: 0.5335... Generator Loss: 2.1068
Epoch 1/10... Discriminator Loss: 1.1917... Generator Loss: 1.1016
Epoch 1/10... Discriminator Loss: 0.4819... Generator Loss: 4.6656
Epoch 1/10... Discriminator Loss: 0.5227... Generator Loss: 2.1578

Epoch 1/10... Discriminator Loss: 0.4688... Generator Loss: 2.4570
Epoch 1/10... Discriminator Loss: 0.6469... Generator Loss: 1.5095
Epoch 1/10... Discriminator Loss: 0.8768... Generator Loss: 4.8754
Epoch 1/10... Discriminator Loss: 0.9485... Generator Loss: 1.1449
Epoch 1/10... Discriminator Loss: 0.6247... Generator Loss: 3.0050

Epoch 1/10... Discriminator Loss: 0.8054... Generator Loss: 1.6262
Epoch 1/10... Discriminator Loss: 0.5066... Generator Loss: 2.8196
Epoch 1/10... Discriminator Loss: 0.5142... Generator Loss: 5.1741
Epoch 1/10... Discriminator Loss: 0.7886... Generator Loss: 2.6137
Epoch 1/10... Discriminator Loss: 0.8016... Generator Loss: 1.7388

Epoch 1/10... Discriminator Loss: 1.0370... Generator Loss: 8.5206
Epoch 1/10... Discriminator Loss: 0.3652... Generator Loss: 4.6717
Epoch 1/10... Discriminator Loss: 0.6319... Generator Loss: 3.4633
Epoch 1/10... Discriminator Loss: 0.3625... Generator Loss: 5.7592
Epoch 1/10... Discriminator Loss: 0.5019... Generator Loss: 2.2287

Epoch 1/10... Discriminator Loss: 1.9111... Generator Loss: 0.2733
Epoch 1/10... Discriminator Loss: 1.1784... Generator Loss: 0.6930
Epoch 1/10... Discriminator Loss: 0.7013... Generator Loss: 8.6799
Epoch 1/10... Discriminator Loss: 0.4885... Generator Loss: 9.6107
Epoch 1/10... Discriminator Loss: 0.5655... Generator Loss: 2.1134

Epoch 1/10... Discriminator Loss: 1.4104... Generator Loss: 9.3394
Epoch 1/10... Discriminator Loss: 0.4055... Generator Loss: 3.6484
Epoch 1/10... Discriminator Loss: 0.4248... Generator Loss: 8.2251
Epoch 1/10... Discriminator Loss: 0.3992... Generator Loss: 3.2729
Epoch 1/10... Discriminator Loss: 1.1563... Generator Loss: 0.7427

Epoch 1/10... Discriminator Loss: 0.3839... Generator Loss: 7.8797
Epoch 1/10... Discriminator Loss: 0.4045... Generator Loss: 3.6381
Epoch 1/10... Discriminator Loss: 0.4977... Generator Loss: 4.9559
Epoch 1/10... Discriminator Loss: 0.7233... Generator Loss: 1.2720
Epoch 1/10... Discriminator Loss: 0.3501... Generator Loss: 8.1631

Epoch 1/10... Discriminator Loss: 0.7014... Generator Loss: 1.3913
Epoch 1/10... Discriminator Loss: 0.4659... Generator Loss: 2.5561
Epoch 1/10... Discriminator Loss: 0.3562... Generator Loss: 8.2128
Epoch 1/10... Discriminator Loss: 0.7604... Generator Loss: 1.1921
Epoch 1/10... Discriminator Loss: 0.3709... Generator Loss: 4.5462

Epoch 1/10... Discriminator Loss: 0.3439... Generator Loss: 5.5327
Epoch 1/10... Discriminator Loss: 0.3660... Generator Loss: 4.2713
Epoch 1/10... Discriminator Loss: 0.5145... Generator Loss: 6.2706
Epoch 1/10... Discriminator Loss: 0.5605... Generator Loss: 1.8915
Epoch 1/10... Discriminator Loss: 0.3457... Generator Loss: 5.2823

# import matplotlib.pyplot as plt
# fig, ax = plt.subplots(figsize=(20,8))
# losses = np.array(losses)
# plt.plot(losses.T[0], label='Discriminator Loss')
# plt.plot(losses.T[1], label='Generator Loss')
# plt.title("Training Losses")
# plt.legend()

提交项目¶

提交本项目前，确保运行所有 cells 后保存该文件。

保存该文件为 "dlnd_face_generation.ipynb"，并另存为 HTML 格式 "File" -> "Download as"。提交项目时请附带 "helper.py" 和 "problem_unittests.py" 文件。